Determinants of the Price Paid at Auctions of Contemporary Art for Artworks by Twelve Artists

Abstract

The use of regression modelling to understand how characteristics of artworks, of artists, and of the circumstances of sale affect the price paid at auction is well-established among cultural economists. Drawing on auction sales data provided by Artprice (accessed on 20 March 2022) I use regression modelling to investigate the determinants of the price paid for artworks by twelve artists at auctions of contemporary art over the period from 1984 to 2019. Each of the artists is modelled separately. For nine of the twelve artists, there was a clear preference among collectors for paintings with specific titles rather than untitled paintings or paintings with generic titles such as ‘abstract’ or ‘composition’. For the other explanatory factors included in the models, my analysis complements and re-contextualizes previous scholarship, showing how collectors’ preferences differed between the contexts examined. Size was a stronger driver of the price paid than in the contexts examined in other studies, and for most artists, collectors were not deterred by the largest artworks. Paintings in oil have continued to appeal to some collectors. Although the number of artists looked at is small, there are some suggestive patterns in how the age of the artist at execution affected the auction price, which might merit further investigation. My models also give some insights into change within the auction market for contemporary art.

Keywords

• art auction market
• contemporary art
• history of art
• digital art history
• regression modelling
• titles